Editorial

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4 pages, 177 KiB

Open AccessEditorial

Low-Carbon Water Treatment and Energy Recovery

by Xin Zhao, Lili Dong and Zhaoyang Wang

Appl. Sci. 2023, 13(17), 9758; https://doi.org/10.3390/app13179758 - 29 Aug 2023

Viewed by 756

Abstract

Climate change led by excessive carbon dioxide (CO₂) emissions poses a global challenge [...] Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

Research

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13 pages, 5215 KiB

Open AccessArticle

Simultaneous Phosphate Removal and Power Generation by the Aluminum–Air Fuel Cell for Energy Self-Sufficient Electrocoagulation

by Xiaoyu Han, Hanlin Qi, Youpeng Qu, Yujie Feng and Xin Zhao

Appl. Sci. 2023, 13(7), 4628; https://doi.org/10.3390/app13074628 - 06 Apr 2023

Cited by 2 | Viewed by 1316

Abstract

A self-powered electrocoagulation system with a single-chamber aluminum–air fuel cell was employed for phosphate removal in this study. Electricity production and aluminum hydroxides in solution were also investigated. When the NaCl concentration increased from 2 mmol/L to 10 mmol/L, the phosphate removal increased [...] Read more.

A self-powered electrocoagulation system with a single-chamber aluminum–air fuel cell was employed for phosphate removal in this study. Electricity production and aluminum hydroxides in solution were also investigated. When the NaCl concentration increased from 2 mmol/L to 10 mmol/L, the phosphate removal increased from 86.9% to 97.8% in 60 min. An electrolyte composed of 10 mmol/L of NaCl was shown to obtain a maximum power density generation of 265.7 mW/m². When the initial solution pH ranged from 5.0 to 9.0, 98.5% phosphate removal and a maximum power density of 338.1 mW/m² were obtained at pH 6.0. Phosphate was mainly removed by aluminum hydroxide adsorption. These results demonstrate that the aluminum–air fuel cell can be applied as electricity-producing electrocoagulation equipment. Aluminum–air fuel cells provide an alternative method to meet the goal of carbon neutrality in wastewater treatment compared with traditional energy-consuming electrocoagulation systems. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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23 pages, 9456 KiB

Open AccessArticle

Water Quality Evaluation and Prediction Based on a Combined Model

by Guimei Jiao, Shaokang Chen, Fei Wang, Zhaoyang Wang, Fanjuan Wang, Hao Li, Fangjie Zhang, Jiali Cai and Jing Jin

Appl. Sci. 2023, 13(3), 1286; https://doi.org/10.3390/app13031286 - 18 Jan 2023

Cited by 2 | Viewed by 1962

Abstract

Along with increasingly serious water pollution, water environmental problems have become major factors that hinder the sustainable development of our economy and society. Reliable evaluation of water quality and accurate prediction of water pollution indicators are the key links in water resource management [...] Read more.

Along with increasingly serious water pollution, water environmental problems have become major factors that hinder the sustainable development of our economy and society. Reliable evaluation of water quality and accurate prediction of water pollution indicators are the key links in water resource management and water pollution control. In this paper, the water quality data of Lanzhou Xincheng Bridge section in the Yellow River Basin and Sichuan Panzhihua Longdong section in the Yangtze River Basin were used to establish a water quality evaluation model and a prediction model. For the water quality evaluation model, we constructed the research samples by means of equal intervals and uniform distribution of interpolated water quality index data according to Environmental Quality Standards for Surface Water. The training samples were determined by a stratified sampling method, and the water quality evaluation model was established using a T-S fuzzy neural network. The experimental results show that the highest accuracy achieved by the evaluation model in water quality classification was 94.12%. With respect to the water quality prediction model, we propose ARIMA-WNN, which combines the autoregressive integrated moving average model (ARIMA) and a wavelet neural network (WNN) with the bat algorithm (BA) to determine the optimal weight of each individual model. The experimental results show that the highest prediction accuracy of ARIMA-WNN is 68.06% higher than that of the original model. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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15 pages, 2671 KiB

Open AccessArticle

Study on the Removal of Iron and Manganese from Groundwater Using Modified Manganese Sand Based on Response Surface Methodology

by Han Kang, Yan Liu, Dan Li and Li Xu

Appl. Sci. 2022, 12(22), 11798; https://doi.org/10.3390/app122211798 - 20 Nov 2022

Cited by 3 | Viewed by 1200

Abstract

This study used modified manganese sand as an adsorbent to explore its adsorption effect on iron and manganese ions from groundwater. The effects of pH, manganese sand dosage, and the initial concentration of Fe/Mn on the removal rate of iron and manganese ions [...] Read more.

This study used modified manganese sand as an adsorbent to explore its adsorption effect on iron and manganese ions from groundwater. The effects of pH, manganese sand dosage, and the initial concentration of Fe/Mn on the removal rate of iron and manganese ions were studied through single-factor experiments. Based on the above three factors, a quadratic polynomial model between the adsorption rate and the above factors was established to determine the optimal adsorption conditions. The response surface analysis showed that pH had the most significant effect on the adsorption process. The optimum conditions for the adsorption of iron and manganese ions by modified manganese sand were pH = 7.20, the dosage of manganese sand = 3.54 g/L, and the initial concentration ratio of Fe/Mn = 3.80. The analysis of variance showed that the RSM model could accurately reflect the adsorption process of manganese sand. In addition, we confirmed that the relative error between model predictions and experimental values was close to 1%, proving that the response surface model was reliable. The kinetic data of the manganese sand were described well with the pseudo-second-order model. The isothermal adsorption of iron and manganese ions by modified manganese sand was fitted well using the Langmuir equation. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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23 pages, 4837 KiB

Open AccessArticle

SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection

by Jiawei Zhang, Pingli Ma, Tao Jiang, Xin Zhao, Wenjun Tan, Jinghua Zhang, Shuojia Zou, Xinyu Huang, Marcin Grzegorzek and Chen Li

Appl. Sci. 2022, 12(19), 9902; https://doi.org/10.3390/app12199902 - 01 Oct 2022

Cited by 10 | Viewed by 1962

Abstract

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. [...] Read more.

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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21 pages, 4439 KiB

Open AccessArticle

Comparative Study for Patch-Level and Pixel-Level Segmentation of Deep Learning Methods on Transparent Images of Environmental Microorganisms: From Convolutional Neural Networks to Visual Transformers

by Hechen Yang, Xin Zhao, Tao Jiang, Jinghua Zhang, Peng Zhao, Ao Chen, Marcin Grzegorzek, Shouliang Qi, Yueyang Teng and Chen Li

Appl. Sci. 2022, 12(18), 9321; https://doi.org/10.3390/app12189321 - 17 Sep 2022

Cited by 5 | Viewed by 1634

Abstract

Currently, the field of transparent image analysis has gradually become a hot topic. However, traditional analysis methods are accompanied by large amounts of carbon emissions, and consumes significant manpower and material resources. The continuous development of computer vision enables the use of computers [...] Read more.

Currently, the field of transparent image analysis has gradually become a hot topic. However, traditional analysis methods are accompanied by large amounts of carbon emissions, and consumes significant manpower and material resources. The continuous development of computer vision enables the use of computers to analyze images. However, the low contrast between the foreground and background of transparent images makes their segmentation difficult for computers. To address this problem, we first analyzed them with pixel patches, and then classified the patches as foreground and background. Finally, the segmentation of the transparent images was completed through the reconstruction of pixel patches. To understand the performance of different deep learning networks in transparent image segmentation, we conducted a series of comparative experiments using patch-level and pixel-level methods. In two sets of experiments, we compared the segmentation performance of four convolutional neural network (CNN) models and a visual transformer (ViT) model on the transparent environmental microorganism dataset fifth version. The results demonstrated that U-Net++ had the highest accuracy rate of 95.32% in the pixel-level segmentation experiment followed by ViT with an accuracy rate of 95.31%. However, ResNet50 had the highest accuracy rate of 90.00% and ViT had the lowest accuracy of 89.25% in the patch-level segmentation experiments. Hence, we concluded that ViT performed the lowest in patch-level segmentation experiments, but outperformed most CNNs in pixel-level segmentation. Further, we combined patch-level and pixel-level segmentation results to reduce the loss of segmentation details in the EM images. This conclusion was also verified by the environmental microorganism dataset sixth version dataset (EMDS-6). Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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19 pages, 8827 KiB

Open AccessArticle

An Application of Pixel Interval Down-Sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images

by Jiawei Zhang, Xin Zhao, Tao Jiang, Md Mamunur Rahaman, Yudong Yao, Yu-Hao Lin, Jinghua Zhang, Ao Pan, Marcin Grzegorzek and Chen Li

Appl. Sci. 2022, 12(14), 7314; https://doi.org/10.3390/app12147314 - 21 Jul 2022

Cited by 6 | Viewed by 1590

Abstract

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder–decoder architecture. The pixel interval down-sampling operations are concatenated with [...] Read more.

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder–decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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13 pages, 3608 KiB

Open AccessArticle

Grain Boundary—A Route to Enhance Electrocatalytic Activity for Hydrogen Evolution Reaction

by Ran Jiang, Jianyu Fu, Zhaoyang Wang and Cunku Dong

Appl. Sci. 2022, 12(9), 4290; https://doi.org/10.3390/app12094290 - 24 Apr 2022

Cited by 3 | Viewed by 1611

Abstract

The electrocatalytic hydrogen evolution reaction (HER) of a given metal catalyst is intrinsically related to its electronic structure, which is difficult to alter for further improvement. Recently, it was discovered that the density of grain boundaries (GBs) is mechanistically of great importance for [...] Read more.

The electrocatalytic hydrogen evolution reaction (HER) of a given metal catalyst is intrinsically related to its electronic structure, which is difficult to alter for further improvement. Recently, it was discovered that the density of grain boundaries (GBs) is mechanistically of great importance for catalytic activity, implying that GBs are quantitatively correlated with the active sites in the HER. Here, by modeling the atomistic structure of GBs on a Au(110) surface, we find that HER performance is greatly enhanced by Au GBs, suggesting the feasibility of the HER mediated by GBs. The promoted HER performance is due to an increase in the capability of binding adsorbed hydrogen on the sites around GBs. A Au catalyst with a dominantly exposed (110) plane is synthesized, where considerable GBs exist for experimental verification. It is found that HER activity is inherently correlated with the density of the GBs in Au NPs. The improvement in HER activity can be elucidated from the geometrical and electronic points of view; the broken local spatial symmetry near a GB causes a decrease in the coordination numbers of the surface sites and the shift up of the d–band center, thereby reducing the limiting potential for each proton−electron transfer step. Our finding represents a promising means to further improve the HER activity of a catalyst. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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12 pages, 3658 KiB

Open AccessArticle

Evaluation of Non-Biodegradable Organic Matter and Microbial Community’s Effects on Achievement of Partial Nitrification Coupled with ANAMMOX for Treating Low-Carbon Livestock Wastewater

by Mingchuan Zhang, Xi Chen, Xinyang Xu, Zhongtian Fu and Xin Zhao

Appl. Sci. 2022, 12(7), 3626; https://doi.org/10.3390/app12073626 - 02 Apr 2022

Cited by 5 | Viewed by 1816

Abstract

After the anaerobic digestion of livestock manure, high concentrations of nutrients still remain. Treatment of livestock wastewater through partial nitrification coupled with anaerobic ammonium oxidation (ANAMMOX) could be a useful technology depending on the investigation of microorganism enrichment and partial nitrification coupled with [...] Read more.

After the anaerobic digestion of livestock manure, high concentrations of nutrients still remain. Treatment of livestock wastewater through partial nitrification coupled with anaerobic ammonium oxidation (ANAMMOX) could be a useful technology depending on the investigation of microorganism enrichment and partial nitrification coupled with achievement of the ANAMMOX process. The results show 78.4% and 64.7% nitrite accumulation efficiency was successfully obtained in an intermittent aeration sequencing batch reactor and a continuous aeration sequencing batch reactor, respectively, at a loading rate of 0.93 kg ammonium/(m³·d). The main reason for the high nitrite accumulation efficiency was the intermittent aeration strategy which generated a 20–30 min lag reaction for nitrite oxidation and promoted the growth of the dominant ammonium oxidation bacteria (Nitrosomonas). Non-biodegradable organic matter in the effluents of partial nitrification did not have obvious influence on ANAMMOX activity at low loading rates (118 ± 13 mg COD/L and 168 ± 9 mg COD/L), and up to 87.4% average nitrite removal rate was observed. However, with the influent COD concentration increasing to 242 ± 17 mg/L, the potential inhibition of ANAMMOX activity was exerted by non-biodegradable organic matter. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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Review

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19 pages, 1392 KiB

Open AccessReview

Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization

by Demin Li, Zhaoyang Wang, Yixuan Yang, Hao Liu, Shuai Fang and Shenglin Liu

Appl. Sci. 2023, 13(3), 1400; https://doi.org/10.3390/app13031400 - 20 Jan 2023

Cited by 5 | Viewed by 3318

Abstract

With the rapid development of the social economy, the demand for water resources is gradually increasing, and the corresponding impact of water pollution is also becoming more severe. Therefore, the technology of sewage treatment is developing rapidly, but corresponding problems also arise. The [...] Read more.

With the rapid development of the social economy, the demand for water resources is gradually increasing, and the corresponding impact of water pollution is also becoming more severe. Therefore, the technology of sewage treatment is developing rapidly, but corresponding problems also arise. The requirements of energy conservation and emissions reduction under the goal of carbon neutrality and dual carbon pose a challenge to the traditional concept of sewage treatment, and there is an urgent need for low-carbon sewage treatment technology aiming at energy conservation, consumption reduction and resource reuse. This review briefly introduces conventional sewage treatment technology and low-carbon sewage treatment technology, and analyzes the research status and development trend of low-carbon sewage treatment technology in detail. The analysis and comparison of conventional and low-carbon sewage treatment technologies is expected to provide a theoretical basis for the practical engineering application of low-carbon sewage treatment technologyto achieve the goal of carbon neutrality. It is of great significance to promote the sustainable development of society and the economy. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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31 pages, 18282 KiB

Open AccessReview

Recent Progress in ZnO-Based Nanostructures for Photocatalytic Antimicrobial in Water Treatment: A Review

by Ziming Xin, Qianqian He, Shuangao Wang, Xiaoyu Han, Zhongtian Fu, Xinxin Xu and Xin Zhao

Appl. Sci. 2022, 12(15), 7910; https://doi.org/10.3390/app12157910 - 07 Aug 2022

Cited by 10 | Viewed by 2338

Abstract

Advances in nanotechnology have led to the development of antimicrobial technology of nanomaterials. In recent years, photocatalytic antibacterial disinfection methods with ZnO-based nanomaterials have attracted extensive attention in the scientific community. In addition, recently widely and speedily spread viral microorganisms, such as COVID-19 [...] Read more.

Advances in nanotechnology have led to the development of antimicrobial technology of nanomaterials. In recent years, photocatalytic antibacterial disinfection methods with ZnO-based nanomaterials have attracted extensive attention in the scientific community. In addition, recently widely and speedily spread viral microorganisms, such as COVID-19 and monkeypox virus, have aroused global concerns. Traditional methods of water purification and disinfection are inhibited due to the increased resistance of bacteria and viruses. Exploring new and effective antimicrobial materials and methods has important practical application value. This review is a comprehensive overview of recent progress in the following: (i) preparation methods of ZnO-based nanomaterials and comparison between methods; (ii) types of nanomaterials for photocatalytic antibacterials in water treatment; (iii) methods for studying the antimicrobial activities and (iv) mechanisms of ZnO-based antibacterials. Subsequently, the use of different doping strategies to enhance the photocatalytic antibacterial properties of ZnO-based nanomaterials is also emphatically discussed. Finally, future research and practical applications of ZnO-based nanomaterials for antibacterial activity are proposed. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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17 pages, 1234 KiB

Open AccessEditor’s ChoiceReview

A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances

by Yang Liu, Tingyu Li, Jia Bao, Xiaomin Hu, Xin Zhao, Lixin Shao, Chenglong Li and Mengyuan Lu

Appl. Sci. 2022, 12(4), 1941; https://doi.org/10.3390/app12041941 - 12 Feb 2022

Cited by 12 | Viewed by 4325

Abstract

In recent years, an increasing amount of short-chain perfluoroalkyl substance (PFAS) alternatives has been used in industrial and commercial products. However, short-chain PFASs remain persistent, potentially toxic, and extremely mobile, posing potential threats to human health because of their widespread pollution and accumulation [...] Read more.

In recent years, an increasing amount of short-chain perfluoroalkyl substance (PFAS) alternatives has been used in industrial and commercial products. However, short-chain PFASs remain persistent, potentially toxic, and extremely mobile, posing potential threats to human health because of their widespread pollution and accumulation in the water cycle. This study systematically summarized the removal effect, operation conditions, treating time, and removal mechanism of various low carbon treatment techniques for short-chain PFASs, involving adsorption, advanced oxidation, and other practices. By the comparison of applicability, pros, and cons, as well as bottlenecks and development trends, the most widely used and effective method was adsorption, which could eliminate short-chain PFASs with a broad range of concentrations and meet the low-carbon policy, although the adsorbent regeneration was undesirable. In addition, advanced oxidation techniques could degrade short-chain PFASs with low energy consumption but unsatisfied mineralization rates. Therefore, combined with the actual situation, it is urgent to enhance and upgrade the water treatment techniques to improve the treatment efficiency of short-chain PFASs, for providing a scientific basis for the effective treatment of PFASs pollution in water bodies globally. Full article

(This article belongs to the Special Issue Low Carbon Water Treatment and Energy Recovery)

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